Research on Optimization of Cut−off Grade in Polymetallic Mines Using Imperialist Competitive Algorithm
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摘要:
边界品位是矿山开采的一个重要决策参数，在多金属矿山开采项目中，确定合理的边界品位是为后续开采获取更大经济效益的基础。针对某矿山采选二阶段生产流程，以最大净现值法为基础，利用综合品位构建了基于帝国竞争算法（ICA）算法的多金属矿山边界品位动态优化模型，实现了该银铅矿最佳边界品位的动态确定。实例应用表明：该模型适用于多金属矿山边界品位的确定。在矿山寿命期间，通过ICA算法所确定的铅金银多金属矿最佳铅边界品位为2.619%，后期下降至1.331%，矿山总净现值为
127457.53 万元；对比Lane法，该模型具有全局搜索能力，对矿山后期边界品位指标的动态优化更具优势，为矿山确定合理的边界品位指标提供了新思路。Abstract:Cut−off grade is an important decision parameter in mining operations. In multi metal mining projects, determining a reasonable cut−off grade is the foundation for obtaining greater economic benefits for subsequent mining. Based on the maximum net present value method, a dynamic optimization model for the comprehensive cut−off grade of a polymetallic mine based on ICA algorithm was constructed for the two−stage production process of mining and selection. The optimal cut−off grade of a certain silver lead mine was dynamically determined. The example application shows that the model is suitable for determining the cut−off grade of polymetallic mines. During the lifespan of the mine, the optimal cut−off grade for a Pb−Au−Ag polymetallic material determined by the ICA algorithm was 2.619%, which later decreased to 1.331%. The total net present value of the mine was
1274.5753 million yuan; Compared with the Lane method, this model has global search ability and is more advantageous in dynamic optimization of cut−off grade indicators in the later stage of mining, providing new ideas for determining reasonable cut−off grade indicators in mines. -
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表 1 矿床经济参数表
Table 1. Economic parameters of ore deposit
指标 采矿成本/(元·t−1) 选矿成本/(元·t−1) 年固定成本/万元 开采能力/(万t·a−1) 选矿能力/(万t·a−1) 折现率/% 数值 171 72 3816 23 15 15 指标 选矿回收率 精矿品位 价格 Pb/% Ag/% Au/% Pb/% Ag/(g·t−1) Au/(g·t−1) Pb/(元·t−1) Ag/(元·t−1) Au/(元·t−1) 数值 91.32 92.67 92.01 40 2602 29.84 13720 3.33 302.55 
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表 2 ICA算法结果
Table 2. Results of ICA
年份 边界品位 开采量/t 选矿量/t 精矿量/t 现金流量/元 累计净现值/元 1 0.026187 230000 150000 20488.42 192811070.1 1274415276 2 0.026187 230000 150000 20488.42 192811070.1 1272766498 3 0.026187 230000 150000 20488.42 192811070.2 1270870403 4 0.026187 230000 150000 20488.42 192811070.2 1268689893 5 0.026187 230000 150000 20488.42 192811070.2 1266182307 …… …… …… …… …… …… …… 31 0.026187 230000 150000 20488.42 192811078.3 557616793.2 32 0.026187 230000 150000 20488.42 192811081.4 448448241 33 0.023492 212567.1 150000 19591.26 183483088.2 328757845.9 34 0.016052 174485 150000 17315.01 158765044.3 201402698.9 35 0.01331 89025.88 81774.53 9017.471 81805064.02 40009635.65
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